I want to do some data analysis using Excel and this tasks I need to answer.
*data clustering
*margine
*seasonality/forecast
Can somebody give me an input maybe?
I am not coming from financial/statistical/analytical field, and these things are more less new to time, but I would like to learn how to do this in Excel.
Here is my data set, where time axis is non consecutive (for forecast prediction for instance).
Related
EDIT1: download file with 2 days of real data
My home automation controller collects data from several 4-in-1 motion sensors in different rooms of my house. The sensor prioritizes motion, sending motion reports every few seconds, but also independently reports temperature, humidity, and illuminance. I am trying to determine if the temp and humidity reports are sent frequently enough to automate control of heaters and exhaust fans.
Sensors independently report each category to the controller, which sends data to excel. Sample data below, but without motion reports that clutter up the real data.
A pivot table generated from the raw data:
Answering the question of frequency takes me several manual steps. Sorting/filtering the dataset for temp/humidity by room, then manually adding a time diff column
where time diff = (<current Date-Time cell> - <prev Date-Time cell>)*24*60. I then calculate the average and stdev of minutes between reports by manually selecting, in turn, each room/category subset in the time diff column; once for the average and once for the stdev.
After a few more manual steps, I end up with this desired result:
BUT I have to do it all over every time new data is added to the table. I'm certain excel can do this automatically, but I didn't find a solution through pivots, power pivots, slicing, or queries. I'm hoping one of you excel gurus can help. Thanks!
Im trying to build an excel sheet that calculates synthetic options prices and greeks for time series data to model intraday options pricing, input is simply intraday price data, say Tick level to 5 minute interval. I found this https://www.thebiccountant.com/2021/12/28/black-scholes-option-pricing-with-power-query-in-power-bi/ which provides for powerBI and Black Scholes but possibly not very accurately. I prefer the Binomial method (I have used this excellent tutuorial to build a manual version for a large number of strikes but it takes a long time to calculate and is very very complex and also inaccurate due to not being able to calculate many steps before topping excel out: https://www.macroption.com/binomial-option-pricing-excel/).
Does anyone have any idea if this is possible to create an entire column in Power Query that will calculate bionomially derived options pricing using >100 even up to 1000 steps? The reason is intraday pricing using high resolution data 5min, 1min, Seconds and Tick I think needs a large number of steps to properly converge. This is just about doing a good enough model that can be used for visualising the progress of a trade on a given day.
Any pointers on how this could be done and calculated using M Language would be much appreciated and useful!
I was trying to plot some reports for Covid-19 cases around the Globe, using Excel and Power BI. With Power BI is easier and fancier to do definitely, but I need an Excel file or calculation that makes sense - similar to the PBI. What I actually wanted is to calculate the daily increase in new cases (with %) and also death rate but per day, or total death by day and so on..
I did some calculations (% of column total and I calculated one field to get death rate%) here using Pivot tables but not sure how to do daily increase/decrease? Did anyone get an idea for additional calculations?
This is copied from PBI (calculations) which I wanna have similar in Excel - but I am not sure If I can calculate it properly (last 2 pictures).
The data source from the input data is here:
https://www.ecdc.europa.eu/sites/default/files/documents/COVID-19-geographic-disbtribution-worldwide.xlsx
You need an extra column for the result you want (e.g. daily increase/decrease), then you can plot either the waterfall chart, or using techniques similar to
https://www.extendoffice.com/documents/excel/5945-excel-chart-display-percentage-change.html
I am trying to create a forecast using a monthly timeseries data set of marketing expenses for a fictional company. The data looks something like this:
Using linear regression to forecast future sales, I get the following result:
My problem lies with the seasonality of the marketing expenses (higher in the summer months for instance). I would ideally like to calculate to forecasted values of future months including seasonality. I read somewhere about ARIMA forecasting, but am really searching for some best ideas on how to accomplish the task.
To be clear, I do not JUST want a chart and trendline, but the data to support it too.
Any help would be much appreciated!
You can do that easily using Excel (2016) Forecast tool by first selecting your data, then clicking on:
Data -> Forecast Sheet -> Options -> Set Manually (under Seasonality)
You can also play with the options. Once you click on "Create", Excel will generate a graph, and a table with relevant data.
Alternatively, you can also create a binary variable for each season, and calculate a multiple regression for the Marketing expenses controlling for time, and each of the binary variables for the seasons but one (which is the reference group). You could either use excel analytical tool, or any other statistical software.
I have a database with time series data of different solar power plants: how strong was the sun and how much power that plant created / harvested. This data is in 15 min increments.
I would like to use data mining to get new insights and to then visualize the findings to the users.
I know this falls into the domain of data mining, but my problem is maybe more specific (dealing with time series data). So what can I extract from this kind of data or where can I read about this?
Time Series Analysis is a whole field in itself. That said, you can always start with a few basics and keep adding more to your analysis.
Here are a few things to try for starters from your solar power data:
First, profile your solar power data. That is, calculate Min, Max, daily averages, hourly peaks and lows etc. to get a feel for the data. Plotting with x-axis as time will give you visual information.
Time Series data can be decomposed into "Trend" & "Seasonality" (can be for any repeating time interval)
Look for outliers, abnormalities in your data stream. Missing values, repeats etc.
If you want to learn more about time-series, (and if know R) then the forecast package is a good way to get started. (Especially this free e-book)
Any search on Time Series will take you to Prof. Hyndman's pages, and I have found the free chapters of his forecasting book very useful.
Hope that helps you get started.